Search

A custom system has been developed for medical image acquisition and processing in both the visible and the infrared (IR) bands. Unlike some non-customizable commercial devices, this system can easily be adapted to different application scenarios by adding new peripherals and/or custom image processing algorithms. Portable, autonomous, and easy to use, it offers immediate results at the moment of examination and has a competitive cost. The system comprises a Single Board Computer (SBC) controlling a group of sensors and peripherals. The hardware implementation, described in detail in this paper, was adapted for two different application scenarios. First, the system was employed to differentiate between different kinds of vascular anomalies. The clinical results obtained are reported. The device was then redesigned to automatically detect people with high body temperatures in public environments. The system’s real-time image processing capabilities in both scenarios are demonstrated. Specific algorithms were implemented by the authors for each case study. [ABSTRACT FROM AUTHOR]

Real-time globally consistent GPS tracking is critical for an accurate localization and is crucial for applications such as autonomous navigation or multi-robot mapping. However, under challenging environment conditions such as indoor/outdoor transitions, GPS signals are partially available or not consistent over time. In this paper, a real-time tracking system for continuously locating emergency response agents in challenging conditions is presented. A cooperative localization method based on Laser-Visual-Inertial (LVI) and GPS sensors is achieved by communicating optimization events between a LiDAR-Inertial-SLAM (LI-SLAM) and Visual-Inertial-SLAM (VI-SLAM) that operate simultaneously. The estimation of the pose assisted by multiple SLAM approaches provides the GPS localization of the agent when a stand-alone GPS fails. The system has been tested under the terms of the MALIN Challenge, which aims to globally localize agents across outdoor and indoor environments under challenging conditions (such as smoked rooms, stairs, indoor/outdoor transitions, repetitive patterns, extreme lighting changes) where it is well known that a stand-alone SLAM will not be enough to maintaining the localization. The system achieved Absolute Trajectory Error of 0.48%, with a pose update rate between 15 and 20 Hz. Furthermore, the system is able to build a global consistent 3D LiDAR Map that is post-processed to create a 3D reconstruction at different level of details. [ABSTRACT FROM AUTHOR]

A bio-sensing scheme, which acquires impedance information of a capacitive biosensor by using the reflected RF signal from a surface acoustic wave (SAW) resonator connected to the biosensor, is proposed. This technique requires no power to be supplied to the biosensor node and hence is highly applicable to wearable applications. Theoretical analysis has demonstrated that the sensitivity of the SAW resonator-coupled biosensor is higher than that of traditional impedance loaded SAW sensors and therefore it is more suitable for measuring the very small impedance changes in biosensors. The passive detection of the change in the impedance of a capacitive biosensor, as a result of biological binding events associated with the capture of a target analyte, has been demonstrated by preliminary experimentation. Dry tests of the SAW coupled capacitive biosensor using a cable connected network analyzer showed the aF level capacitance measurement resolution, which was only achieved in transistor level circuits previously, could be attained. When liquid samples with concentrations of C-Reactive Protein (CRP) in the range of 0.1 to $2 ~\mu \text{g}$ /ml were applied to the biosensor, a corresponding change in the resonant frequency of the SAW resonator-coupled biosensor (in the order of sub-hundred kHz) was observed. This has demonstrated the potential for applying this technique in applications where a zero-power requirement at the biosensor node could be a distinct advantage, when the cable link between the network analyzer and the biosensor node is replaced by the RF transmission. [ABSTRACT FROM AUTHOR]

Sensor applications often face three main restrictions: power consumption, cost, and lack of infrastructure. Most of the challenges imposed by these limitations can be addressed by embedding Machine Learning (ML) classifiers in the sensor hardware, creating smart sensors able to interpret the low-level data stream. However, for this approach to be cost-effective, we need highly efficient classifiers suitable to execute in resource-constrained hardware, such as low-power microcontrollers. In this paper, we present an open-source tool named EmbML – Embedded Machine Learning that implements a pipeline to develop classifiers for resource-constrained hardware. We describe EmbML implementation details and comprehensively analyze its classifiers considering accuracy, classification time, and memory usage. Moreover, we compare the performance of EmbML classifiers with classifiers produced by related tools to demonstrate that our tool provides a diverse set of classification algorithms that are both compact and accurate. Finally, we validate EmbML classifiers to recognize disease vector mosquitoes in a smart sensor and trap application. [ABSTRACT FROM AUTHOR]

This paper presents a fault tolerance architecture for data fusion mechanisms that tolerates sensor faults in a multirotor Unmanned Aerial Vehicle (UAV). The developed approach is based on the traditional duplication/comparison method and is carried out via error detection and system recovery to both detect and isolate the faulty sensors. It is applied on an informational framework using extended Informational Kalman Filters (IKF) for state estimation with prediction models based on available sensors measurements. Error detection is realized through residuals comparisons using the Bhattacharyya Distance (BD), an informational measure that estimates the similarity of two probability distributions. An optimal thresholding based on Bhattacharyya criterion is applied. In order to identify the faulty sensor, the Bhattacharyya distance between the a priori and a posteriori distributions of each IKF is also computed. The system recovery is done by substituting the erroneous state by an error-free state. The proposed architecture alleviates the assumption of a fault-free prediction model using the information surprise concept instead of hardware redundancy.The performance of the proposed framework is shown through offline validation using real measurements from navigation sensors of a multirotor UAV with fault injection. [ABSTRACT FROM AUTHOR]

In recent times, Wireless Sensor Network (WSN) technology has received significant attention owing to its numerous applications in various mission critical services, such as healthcare, military monitoring, public safety systems, and forest monitoring. However, sensor node’s low computational power and inadequate advancements in battery design hinder resource-rigorous applications from fully exploiting WSN capabilities. The trend of shifting computations and storage to remote clouds offers the opportunity to integrate WSNs into the cloud to mitigate their limitations. The Sensor Cloud augments WSN capabilities of remote sensing and monitoring while exploiting resource-rich cloud infrastructure. To date, numerous solutions have been proposed to handle the integration concerns of WSNs with the cloud. However, a comprehensive study that covers various Sensor Cloud aspects in terms of architecture, network dynamics, heterogeneity, communication patterns, data management, and security, is still lacking. To fill this gap, state-of-the-art sensor cloud integration frameworks are analyzed in this article. Moreover, a detailed thematic taxonomy is presented to classify existing sensor cloud frameworks while focusing in service oriented and client server architectures. Related features and critical aspects of existing frameworks are inspected through a qualitative analysis based on the parameters selected from the literature and extracted from the taxonomy. Several research opportunities in this research domain are suggested to assist researchers in identifying and designing optimal resource sensor cloud integration frameworks to host emerging applications. [ABSTRACT FROM AUTHOR]

Improvement of data acquisition rate remains as an important challenge in applications with Quartz Crystal Microbalance (QCM) technology where high throughput is required. To address this challenge, we developed a fast method capable of measuring the response of a large number of sensors and/or overtones, with a high time resolution. Our method, which can be implemented in a low-cost readout electronic circuit, is based on the estimation of $\Delta {f}_{r}$ (frequency shift) and $\Delta {D}$ (dissipation shift) from measurements of the sensor response obtained at a single driving frequency. By replacing slow fitting procedures with a direct calculation, the time resolution is only limited by the physical characteristics of the sensor (resonance frequency and quality factor), but not by the method itself. Capabilities of the method are demonstrated by monitoring multiple overtones with a single 5 MHz sensor and a Monolithic QCM array comprising 24 50MHz-sensors. Accuracy of the method is validated and compared with the state-of-the-art, as well as with a reference method based on impedance analysis. [ABSTRACT FROM AUTHOR]

Reading resistive sensors and converting their information to digital values is a matter of great interest in practical applications. Direct Interface Circuits (DICs) perform this task efficiently through a resistance-to-time-to-digital conversion. The main benefit of this type of circuit is the simplicity of its design and the accuracy of the results. However, one of its drawbacks is that quantization errors in the measurements seriously compromise accuracy if the information is not converted using a high-frequency clock or with high capacitor values. To avoid this situation, this article presents a new type of DIC with the same passive elements as a classic DIC, but which uses a high value resistor in the final instant of the resistance-to-time conversion. The circuit has been implemented using a commercial FPGA with the capture module operating at different clock frequencies. The results show that relative errors in measuring resistors of up to $33~\Omega $ decrease to 1.56% when operating with a 12.5 MHz timer. [ABSTRACT FROM AUTHOR]

Completely eliminating crosstalk effect is the most important challenge when reading a resistive sensor array with ${M}$ rows and ${N}$ columns. It may require complex electronics, ranging from a large number of multiplexers and switches to use of ${M}$ AC voltage sources. More measurements than those considered strictly necessary, ${M}\,\cdot {N}$ , may also be required. Thus, an implementation with multiplexers and switches uses $2\,\cdot {M}\,\cdot {N}$ measurements, while this number rises to ${M}\,\cdot {N}\,\cdot {L}$ in the one that uses AC voltage sources, where L is the Fourier Transform length required to obtain the sensor readings. This article presents a new circuit and readout method to eliminate crosstalk that simultaneously reduces the total number of multiplexers to ${M}+{N}$ and the number of measurements to ${N}\,\cdot ({M}+1+({N}+1$)/2). The unique source of error comes from the non-idealities of the only operational amplifier used. As an added benefit, the new circuit also reduces power consumption of the array by including a single additional resistor. [ABSTRACT FROM AUTHOR]

Acoustic reflective delay lines are ideally suited as cooperative targets for passive wireless sensing: a dedicated temperature sensor was designed and manufactured to demonstrate passive bi-static measurement using 25 kW terrestrial video broadcast emission at a range of 4850 m, with a continuous refresh rate of a measurement every second. The sensor was buried 5 cm deep in sand with a reader antenna located 10 cm from the sensor, a deployment scenario representative of distributed passive sensors for civil engineering monitoring. [ABSTRACT FROM AUTHOR]

A multimodal automatic identification technology that can detect and monitor the movement of low dose radiation and communicate changing environmental conditions can prevent and/or reduce the negative impacts related to nuclear threats. This paper seeks to capitalize on scintillation nanoparticles research that has a better sensitivity and higher energy resolution for radiation detection than current systems. In summary, it is hypothesized that by further developing this research, any attempts to transport radiological materials in the U.S. will be minimized, or even preventable, through improved detection and monitoring. This section of the research evaluates the costs associated with the development of AutoID devices in order to make deployment more economical. [ABSTRACT FROM PUBLISHER]

Body Sensor Networks (BSNs) represent an emerging technology which has received much attention recently due to its enormous potential to enable remote, real-time, continuous and non-invasive monitoring of people in health-care, entertainment, fitness, sport, social interaction. Signal processing for BSNs usually comprises of multiple levels of data abstraction, from raw sensor data to data calculated from processing steps such as feature extraction and classification. This paper presents a multi-layer task model based on the concept of Virtual Sensors to improve architecture modularity and design reusability. Virtual Sensors are abstractions of components of BSN systems that include sensor sampling and processing tasks and provide data upon external requests. The Virtual Sensor model implementation relies on SPINE2, an open source domain-specific framework that is designed to support distributed sensing operations and signal processing for wireless sensor networks and enables code reusability, efficiency, and application interoperability. The proposed model is applied in the context of gait analysis through wearable sensors. A gait analysis system is developed according to a SPINE2-based Virtual Sensor architecture and experimentally evaluated. Obtained results confirm that great effectiveness can be achieved in designing and implementing BSN applications through the Virtual Sensor approach while maintaining high efficiency and accuracy. [ABSTRACT FROM PUBLISHER]

This paper presents a novel ultrasonic sensing system for autonomous mobile systems. We describe how wide-angled ultrasonic transducers can be used to obtain substantial information from the environment. This can be achieved by exploiting the overlapping of detection cones from neighbor sensors and by receiving cross echoes between them. The ultrasonic sensing system also allows the detection of multiple echoes from different echo paths for each sensor. In this way, a significantly higher number of echoes can be obtained in comparison to conventional ultrasonic sensing systems for mobile robots. In order to benefit from the increased sensor information, algorithms for adequate data post-processing are required. In this context, we describe how an environment model can be created from ultrasonic sensor data. [ABSTRACT FROM PUBLISHER]